Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks

Bennett, Christopher J.; Stewart, Rodney Anthony; Lu, Junwei

doi:10.3390/en7052938

Cited by 70 publications

(47 citation statements)

References 25 publications

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“…Specifically, two different statistical models are used in this study. These models are multiple linear regression model [12][13][14] and Box-Jenkins' autoregressive model of order 1 [15][16][17][18][19]. It also provides the source and method of data collection.…”

Section: Methodsmentioning

confidence: 99%

Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1

Ebukanson¹,

Chidi²,

Iriaoghuan³

2017

IJEPE

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Abstract:This study presents statistical analysis of electricity generation in Nigeria using two different statistical models, namely; multiple linear regression model and box-Jenkins' autoregressive model of order 1. Two climatic variables (rainfall and temperature) were used as the explanatory variables. Data on electricity generation in Nigeria between 2002 and 2014 were obtained from the Central Bank of Nigeria Statistical Bulletin while Data on rainfall and temperature between 2002 and 2014 were extracted from the National Bureau of Statistics (NBS) abstract. Test of model fitness and forecasting accuracy were done using generic statistical approach which include coefficient of determination and root mean square error. The prediction accuracy of the two statistical models was compared and the best model was selected. Furthermore, correlation between power generation and the two climatic variables (rainfall and temperature), were carried out and the result reveals that the amount of rainfall has significant and positive relationship with power generation in Nigeria. Specifically, rainfall has correlation value of r = 0.927 with the power generation at probability, p = 0.000 and the relationship was significant at 1% (p<0.01). However, temperature although it is positively correlated, does not significantly affect power generation. Temperature has correlation value of t = 0.136 with power generation at probability, p = 0.658 (p>0.05) and the relationship was significant at 5% (p<0.05). Among the two statistical models, multiple linear regression model was selected as the best model as it gave the highest value of coefficient of determination (r 2 =99.77%) and the least Root Mean Square Error (60.27%).

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Section: Methodsmentioning

confidence: 99%

Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1

Ebukanson¹,

Chidi²,

Iriaoghuan³

2017

IJEPE

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show abstract

“…The difference between the two is that ARIMAX has an exogenous input, in addition to the auto-regressive and moving averages parameters [15]. The ARIMAX model can be understood as the combination of the auto-regressive (p), integrated (d), moving average (q), and exogenous (r) models, which can then be symbolized as ARIMAX(p,d,q,r).…”

Section: Box-jenkins and Box-tiao Modelingmentioning

confidence: 99%

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

et al. 2018

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This work proposes hybrid models combining time-series models (using linear functions) and artificial intelligence (using a nonlinear function) that can be used to provide monthly mean wind speed predictions for the Brazilian northeast region. These might be useful for wind power generation; for example, they could acquire important information on how the local wind potential can be usable for a possible wind power plant through understanding future wind speed values. To create the proposed hybrid models, it was necessary to set the wind speed variable as a dependent variable of exogenous variables (i.e., pressure, temperature, and precipitation). Thus, it was possible to consider the meteorological characteristics of the study regions. It is possible to verify the hybrid models' efficiency in providing perfect adjustments to the observed data. This statement is based on the low values found in the error statistical analysis, i.e., an error of approximately 5.0% and a Nash-Sutcliffe coefficient near to 0.96. These results were certainly important in predicting the wind speed time-series, which was similar to the observed wind speed time-series profile. Great similarities of maximums and minimums between the series were evident and showed the capacity of the models to represent the seasonality characteristics.

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“…For many years, regressive statistical approaches [15] have been considered for prediction; among them, moving average (MA), auto regressive (AR), auto regressive moving average (ARMA), auto regressive integrated moving average (ARIMA) and autoregressive integrated moving average with exogenous variables (ARIMAX) are typical examples of these approaches. Unfortunately, standard structural models provide a poor representation of actual data and therefore result in poor accuracy when used for forecasting.…”

Section: Introductionmentioning

confidence: 99%

Prediction in Photovoltaic Power by Neural Networks

et al. 2017

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Abstract:The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches.

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Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks

Cited by 70 publications

References 25 publications

Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1

Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

Prediction in Photovoltaic Power by Neural Networks

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